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DeMixT (v 1.20.1)

DeMixT is a frequentist-based method and fast in yielding accurate estimates of cell proportions and compartment-specific expression profiles for two- and three-component deconvolution from heterogeneous tumor samples.

Updates

(06/23/2024) Script DeMixT_preprocessing.R now inside the R package for input data preprocessing, to be consistent with the bioconductor version 1.20. The DeMixT tutorial at https://wwylab.github.io/DeMixT/ is updated.

(06/18/2022) Fixed a bug in R/DeMixT_GS.R:130: changed return(class(try(solve(m), silent = T)) == "matrix") to return(class(try(solve(m), silent = T))[1] == "matrix") to make it work properly under both R 3.x and R 4.x; since under R 4.x, class(matrix object) returns "matrix", "array", instead only "matrix" under R 3.x. Please be aware of this bug for those who installed DeMixT(v1.10.0) from Bioconductor.

Installation

The DeMixT package is compatible with Windows, Linux and MacOS. Specifically, for Linux and MacOS, the user can install the latest DeMixT (v 1.20.1) from GitHub:

if (!require("devtools", quietly = TRUE))
    install.packages('devtools')

devtools::install_github("wwylab/DeMixT")

For Windows, we recommend the user to install DeMixT (v 1.20.0) from Bioconductor:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("DeMixT")

Please note, Linux and MacOS users can also install DeMixT from Bioconductor.

Check if DeMixT is installed successfully:

# load package
library(DeMixT)

We highly recommend the user to use DeMixT on Linux or Windows machines.

Issues of DeMixT on MacOS

  1. DeMixT relies on OpenMP for parallel computing. Starting from R 4.00, R no longer supports OpenMP on MacOS, meaning the user can only run DeMixT with one core on MacOS.
  2. We noticed there may be installation/running errors for DeMixT on MacOS machines with M1 chips. We are tring to fix it.

Use DeMixT

A tutorial is available at https://wwylab.github.io/DeMixT/.

Cite DeMixT

[1] Ahn, J. et al. DeMix: Deconvolution for mixed cancer transcriptomes using raw measured data. Bioinformatics 29, 1865–1871 (2013).

[2] Wang, Z. et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience 9, 451–460 (2018).

[3] Cao, S. et al. Estimation of tumor cell total mRNA expression in 15 cancer types predicts disease progression. Nature Biotechnology Published online June 13 2022. doi 10.1038/s41587-022-01342-x.